Predict your customer’s next step with our most advanced module yet

May 02, 2023 | Email Automation, Web Personalization, Search Personalization, E-mail Personalization

By Mads Sieron Thorsen

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Raptor is proud to present our most advanced recommendation module to date. Find out what the new module is – and how it can be fully adjusted to any situation during the user journey.

Get ready! Our newest module is based on 10+ years of expertise in data utilization and personalization. It has been perfected over the past 18 months and tested on 30+ of our customers. All to deliver the most flexible module that drives sales across the entire user journey.

A Personal Shopping Assistant for your website

When you imagine a great shopping experience, you probably think of something like this:
You are looking for a new winter coat, and the assistant guides you around the store. Along the way, she takes note of the styles, brands, and price points you seem to take an interest in. Once you find your favorite coat, she might suggest a matching scarf or a knitted hat that you otherwise wouldn’t have considered. You leave having bought a coat - and maybe something extra - that will be worn and loved for years to come.

This type of experience used to be exclusively reserved for your physical store. Until now, that is!

Raptor's newest recommendation strategy, aptly named Personal Shopping Assistant, is the closest you can get to a personal shopping assistant on your website.

Just like an in-store employee, the module follows you around on the site, instantly adjusting its recommendations to your changing shopping behaviour and intentions.

Accelerate your revenue: Race ahead with Website Personalization

This guide will show you how to personalize your entire website for each individual visitor – all you need to create a personalized customer experience on your website.

Download the guide right here

Your most flexible module

The use cases for this module are endless. Depending on your strategy, the module can boost certain brands and categories, and you can choose how much they should be weighed. You can also choose which similar or cross-selling products should be shown depending on the event type.

For frequent shoppers, the module can be set to show favorite products that they “might have forgotten.”

The module also has a split-testing function, so you can test which settings perform the best.

Importantly, you can control all settings yourself – without help from Raptor’s data scientist team.

Check out this module’s many other, groundbreaking benefits:

The module makes personal recommendations based on both CookieID and UserId (1st party data)
You can choose which products to use as backfilling for different event types
The module can both boost and suppress products. This way, the module can be set to not show product in certain contexts (which you define yourself).
You can choose which similar or cross-selling products should be shown depending on event type.
You decide whether the module should boost certain brands/categories, and how much they should be weighed.
For frequent customers, the module can be set to show favorite products that they “might have forgotten.”
The module has a built-in split-test function, so you can test several settings in the same module

Always one step ahead of the user journey

As a brand-new feature, the module also suppresses certain data points as the customer journey moves forward.

In other words, the algorithm can be instructed to stop showing certain products – and start showing something else. This is an especially useful feature for when the visitor has made their first conversion and can be nudged to make a second one.

Let’s look at both ways to use it:

1. Secure the conversion

When the user first visits your page looking for a pair of trainers, the goal is to show them the trainers they most likely want. If the user is brand-new on your site, the module will base its recommendations on popular trainers, others have looked at - as well as new interactions with the search bar or products and categories the user clicks on.

If the user has visited you before, the module bases its recommendations on previous interests like brands, categories, and new interactions on your site.

While the user is still browsing around your site looking at product pages, it is safe to assume that none of the trainers are quite right. Here, the module will show rows of alternative trainers to the ones the user has looked at – either in similar colors, style, or brand.

You show the user the most relevant recommendations to their intentions at this moment – and the user is more likely to make their first conversion.

2. Secure cross sale(s)

Once the user has found a pair of trainers and placed them in their basket, the module will no longer show trainers. After all, this can create confusion or lead them away from the product they found.

Instead, the module starts predicting which items the user is likely to be interested in next. Maybe a pair of running tights from the same brand. After that, a pair of sports socks.

This module can also be placed in your e-mail marketing flows. Did the user spend several minutes looking at the product page of a specific running jacket? Maybe they clicked on it many times? Then the module is likely to show this jacket in e-mails targeting this customer.

For every step in the customer journey, the module predicts what the user needs next – before they are even aware of it themselves.

That gives you as an e-commerce retailer the best possible conditions to up- and cross-sell in a way that also creates a much better experience for your customers.

More data points than ever before

Real-time Personal Recommendations is our most intelligent and flexible module yet. The algorithm takes data points from every step of the customer journey into account. It not only calculates the customer’s interests and intent. Uniquely, it can accurately predict what your customer needs next.

The module considers more data points than ever, including:

Collaborative data from other users
Online and offline purchases
Products searched for
Products looked at
Interactions
Search data
Basket data
Visit data
Content

… and many other data sources to help show the most relevant recommendations for the user.

How do I use it – and where do I find it?

The new, updated module is available for both web and e-mail recommendations if you are already a user of Web Personalization and E-mail Personalization by Raptor.

The module comes at no further expense and can be found in the control panel by looking for Real-time Personal Recommendations (or the technical name “GetUserItemRecommendationsWeb”).

There are several places you can put the module:

Sidebar
Front page
Login page
Basket page
Powerstep/pop-up

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For the highest conversions, we recommend placing it as a sidebar that follows your customer throughout their visit. This way, the module constantly calculates the best possible recommendations and shows them in real time.